OCR - What does it mean?

To ensure that the individual digits can ultimately be identified, various methods are used depending on the application. On the one hand, the OCR software Tesseract can be used. This was originally developed by Ray Smith at HP Inc. and is now part of Google LLC. Alternatively, the artificial neural network developed by Yann LeCun, better known as the Convolutional Neural Network (CNN), LeNet, is available.

When using Tesseract, the first step is to analyze the correlation components. The contour of the individual components is saved and combined into groups. A polygon approximation then takes place, which searches for straight areas of the contour of a character and thus creates an approximation of the character in the form of a polygon. To minimize the training effort, the polygons are then normalized to uniform dimensions. In the next step, feature vectors are created on the basis of the approximation, which contain the coordinates of a point on the polygon and its inclination. More points mean greater accuracy. Classification then takes place using a k-nearest neighbor algorithm. The approximated polygon is thus compared with the feature vectors of the training data, i.e. characters known to the system, and classified according to the character with the maximum match.

The algorithms now work so well that in most cases there is 99% certainty that the characters will be read correctly.